{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "653089c9",
   "metadata": {},
   "source": [
    "# Appendix: Installing Python, Jupyter Notebook, and PyTorch\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "***\n",
    "***“Life is short (You need Python).”***\n",
    "\n",
    "\n",
    "\n",
    "-- Bruce Eckel, author of *Think in Java* and *Think in C++* \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54ae7c8b",
   "metadata": {},
   "source": [
    "This appendix covers\n",
    "* How to install Anaconda on your computer based on your operating system\n",
    "* Creating a Python virtual environment for projects in this book\n",
    "* Installing Jupyter Notebook in the virtual environment\n",
    "* Installing PyTorch based on whether you have a CUDA-enabled GPU\n",
    "\n",
    "There exist various ways of installing Python and managing libraries and packages on your computer. This book uses Anaconda, an open-source Python distribution, package manager, and environment management tool. Anaconda stands out for its user-friendly nature and capacity to facilitate the effortless installation of numerous libraries and packages, which could be painful or downright impossible to install otherwise.\n",
    "Specifically, Anaconda allows users to install packages through both 'conda install' and 'pip install,' broadening the spectrum of available resources. Subsequently, you will create a dedicated Python virtual environment for all the projects in this book. This segregation ensures that the libraries and packages used in this book remain isolated from any libraries utilized in other, unrelated projects, thus eliminating any potential interference.\n",
    "We will use Jupyter Notebook as our integrated development environment (IDE). I will guide you through the installation of Jupyter Notebook in the Python virtual environment you just created. Finally, I will guide you through the process of installing PyTorch, Torchvision, and Torchaudio, based on whether your computer is equipped with a Compute Unified Device Architecture (CUDA)-enabled GPU.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "282ad876",
   "metadata": {},
   "source": [
    "# 1. Install Python and Set Up a Virtual Environment\n",
    "In this section, I’ll guide you through the process of installing Anaconda on your computer based on your operating system. After that, you’ll create a Python virtual environment for all projects in this book. Finally, you’ll install Jupyter Notebook as your IDE to run Python programs in this book. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d963a4e3",
   "metadata": {},
   "source": [
    "## 1.1. Install Anaconda\n",
    "To install Python through the Anaconda distribution, follow the following steps. \n",
    "First, go to the official Anaconda website at https://www.anaconda.com/products/individual and scroll to the bottom of the webpage. Locate and download the most recent Python 3 version tailored to your specific operating system (be it Windows, macOS, or Linux).\n",
    "\n",
    "If you are using Windows, download the latest Python 3 graphical installer from the above link. Click on the installer and follow the provided instructions to install. To confirm the successful installation of Anaconda on your computer, you can search for the \"Anaconda Navigator\" application on your computer. If you can launch the application, Anaconda has been successfully installed.\n",
    "\n",
    "For macOS users, the latest Python 3 graphical installer for Mac is recommended, although a command line installer option is also available. Execute the installer and comply with the provided instructions. Verify the successful installation of Anaconda by searching for the \"Anaconda Navigator\" application on your computer. If you can launch the application, Anaconda has been successfully installed.\n",
    "\n",
    "The installation process for Linux is more complex than for other operating systems, as there is no graphical installer. Begin by identifying the latest Linux version. Select the appropriate x86 or Power8 and Power9 package. Click to download the latest installer bash script. The installer bash script is typically saved to your computer's \"Downloads\" folder by default. Install Anaconda by executing the bash script within a terminal. Upon completing the installation, activate it by running the following command:\n",
    "\n",
    "`\n",
    "source ~/.bashrc\n",
    "`\n",
    "\n",
    "To access Anaconda Navigator, enter the following command in a terminal:\n",
    "\n",
    "`\n",
    "anaconda-navigator\n",
    "`\n",
    "\n",
    "If you can successfully launch the Anaconda Navigator on your Linux system, your installation of Anaconda is complete."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d2dd43",
   "metadata": {},
   "source": [
    "## 1.2. Create A Virtual Environment\n",
    "It's highly recommended that you create a separate virtual environment for this book. Let’s name the virtual environment dgai. Execute the following command in the Anaconda prompt (Windows) or a terminal (Mac and Linux):\n",
    "\n",
    "`conda create -n dgai`\n",
    "\n",
    "After pressing the ENTER key on your keyboard, follow the instructions on the screen and press y when the prompt asks you y/n. To activate the virtual environment, run the following command in the same Anaconda prompt (Windows) or terminal (Mac and Linux):\n",
    "\n",
    "`conda activate dgai`\n",
    "\n",
    "The virtual environment isolates the Python packages and libraries that you use for this book from other packages and libraries that you use for other purposes. This prevents any undesired interference. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d794163",
   "metadata": {},
   "source": [
    "## 1.3. Set Up Jupyter Notebook in the Virtual Environment\n",
    "Now, let’s install Jupyter Notebook in the newly created virtual environment on your computer.\n",
    "First, activate the virtual environment by running the following line of code in the Anaconda prompt (in Windows) or a terminal (in Mac or Linux):\n",
    "\n",
    "`conda activate dgai`\n",
    "\n",
    "To install Jupyter Notebook in the virtual environment, run the command:\n",
    "\n",
    "`conda install notebook`\n",
    "\n",
    "Follow the instructions all the way through to install the app. \n",
    "To launch Jupyter Notebook, execute the following command in the same terminal with the virtual environment activated:\n",
    "\n",
    "`jupyter notebook`\n",
    "\n",
    "The Jupyter Notebook app will open in your default browser. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56220e33",
   "metadata": {},
   "source": [
    "# 2. Install PyTorch"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "id": "dd4439c5",
   "metadata": {},
   "source": [
    "CUDA is only available on Windows or Linux, not on Mac. To find out if your computer is equipped with a CUDA-enabled GPU, open the Windows PowerShell (in Windows) or a terminal (in Linux), issue the following command: \n",
    "\n",
    "`nvidia-smi`\n",
    "\n",
    "If your computer has a CUDA-enabled GPU, you should see an output similar to figure Appendix.1. Further, make a note of the CUDA version as shown at the top right corner of the figure because you’ll need this piece of information later when you install PyTorch. Figure Appendix.1 shows that the CUDA version is 11.8 on my computer. The version may be different on your computer. \n",
    "\n",
    "![Screenshot%202023-10-22%20173050.png](attachment:Screenshot%202023-10-22%20173050.png)\n",
    "Figure Appendix.1 Checking if your computer has a CUDA-enabled GPU."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ee18ed",
   "metadata": {},
   "source": [
    "## 2.1. Install PyTorch without GPUs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c79fd4a",
   "metadata": {},
   "source": [
    "To install PyTorch with CPU training, first activate the virtual environment by running the following line of code in the Anaconda prompt (in Windows) or a terminal (in Mac or Linux):\n",
    "\n",
    "`\n",
    "conda ctivate dgai\n",
    "`\n",
    "\n",
    "You should be able to see *(dgai)* in front of your prompt, which indicates that you are now in the *dgai* virtual environment. To install PyTorch, issue the following line of command:\n",
    "\n",
    "`\n",
    "conda install pytorch torchvision torchaudio cpuonly -c pytorch\n",
    "`\n",
    "\n",
    "Follow the on-screen instructions to finish the installation. Here we install three libraries together: PyTorch, Torchaudio, and Torchvision. Torchaudio is a library to process audio and signals and we need it to generate music later in this book. We'll also use the Torchvision library extensively in the book to process images.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9b78045",
   "metadata": {},
   "source": [
    "To check if the three libraries are successfully installed, run the following lines of code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "db75506e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.1\n",
      "0.15.2\n",
      "2.0.2\n"
     ]
    }
   ],
   "source": [
    "import torch, torchvision, torchaudio\n",
    "\n",
    "print(torch.__version__)\n",
    "print(torchvision.__version__)\n",
    "print(torchaudio.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14d43783",
   "metadata": {},
   "source": [
    "If you don't see an error message, you have successfully install PyTorch on your computer. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "348c6ec7",
   "metadata": {},
   "source": [
    "## 2.2. Install PyTorch with GPUs"
   ]
  },
  {
   "attachments": {
    "figAppendix02_LIU.png": {
     "image/png": 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NfOV9xe+3IgfFk3JEVkOS2qFyVrOXubTj/XnW+al+14c1sCVBB5QfXj/+i8rM/5zBUfcfMjtcBOArCDwAALzgMVVtat/Dn7V1idJ/sZIVTuayOGdw5Aaq0u0GK+XeDQpq0chKZe35VNlj52vzQp201k0tVek6xJpUcS2QK8le8kphfVUt30BNA1VrZK+v00lxZ9YX4HVpio/bYaUCr2svp1eeiufidurQzE7uSDi3gjA31+Y4bTUzttDrcg1I66/wcLvblv1ffFa+Wg4kJ1ljPAAXngylm91CGgKDqtkJy8/anGDfAVwjsr3Cil0DUNfqg92y/UvFn1XZ/6M2xNm/Uf/Gm3WFN2oZMvZr6879Sjt+Uq7DcZr/fLSmzV+prUlpctmFMelEmvZ/F6PZk1/SWjPW4tqv9XOetYIM683POuvDDFqYwYgPpk/U/O+LGXw4fMDOW+rWKJ3g5g8fapEZBK7dXf0LGNMH8C2lcPwXWa6WSC7f67wXuJAReAAAlFzfMHvwZaUpa8sSa1KFtHmJTjk1Wn6X5zNOQ+t7VMUaB2KfTi3LvT6+VuYP9lgGlRp1rPB99mZ8sNwZG6ORAv45W4EeBmL06zvefIt0Yo9OLDC7pQJKSdaP2v2TnWx8tVOBViH5q0ULZ/l+MJbZTrnhUkKCHYgJa9tOuYdP8G95rV1BeeR7bS5HkYfdu360E1f9QVfYKeDC8PvP2m11jRasK0NzHc0pu3Omt2zptGoqJv+WLWXnLMZv7bEStmQjn7Hq8y9V06vzGYilWA5q7dz3tTWzjiL6PqzHx03Q8y9M1vPPPqOhd7e2x55w7deqRbOsAMSKHzJUJ6KXooY9o2fM970wQWOHDVBrq1IyQ1vfnaMNxehCLu2IPWpM8CWlMcBDmjasiTPmLlARvRjEHuVMqR7/hZWsgwfsVPDFZTUPANypdNrgpHN88+0WJ2W7/rpWTgplLnmVJjwXowOqq15PRasbN0MAF6wjt1/jpHzAox+qxi1mzfEeZd5+h8xeTEpuvEI+6qvKxsXYydntlfahM7kwsufn2NdKvyfK6tM4xx2zdUnUDapUnO81BE74UkHmWBbuvtsRMG6tql1fS/plhdKGjDp7YOXLRitk5gBVrpypkwsjlPaOM91rBit44WOqUt0ctPsapb7sTC6q/NZhEfj1na3gQTfYgalTaTq17nUdXzDXafnRQFWjJqnaHeYg3Mb2eLOf0pZ4Y2BrnE81PvrOSbl39Fhx955ScChW06as1EHV0a0jH1MnL/WXcXDNi5q2+qB0zSA9f19LZ2phJGvtlCladUhqMXCyBp710TOv1ek6UsOLegds8kpNnharI/kt6+/rNOP5GO33C1P/cX9W6zyDKe94f4zmxmco+KaHNdrp392rvp+nJ+d/b93hO3ykFyrbTnyv+RPnWV3KhN31jAZ7o0mLt+cRZeqi6vnvE76QX737ztv67rv881UgW3h4uO4ZcJ/zDBVFecircmSfJ9VSA18Y5Iw1U1JFLAOdiNPcMe/LvJUi7K4Jxvk/uyPN/MpZBcgpM3pzuYCKpaC8ykSLBwBAiQXUcmq0jh1yuiKquE6mptqJyoGqZKfy6Cv/RrWs1Kmt088OOph+mauTP5uJAFWJHF3hT8RZS6KUNtVY5oOZxjoLVuVOjyl4Zrwunjrb+LtY1c2gw4l9cs0k6IDzILurDNVRjdIaHNRX1LhU9mV6utLTrcQ50r771u5GqUXkOUEHU9g1rayWWWnxG7WjHGTuu2Pes/uxr91Zt13nhaADAADI15FDzmDONeuU0vgnBTv42Tor6GCO3xXequKN3gWUZwQeyptLWqn3A4M0+IE+Cr/EmQYAZazSBXQ2OZnhjEAQGOw+8HBPX1WpYfw9tV0n33NXkb5Px7/bbievbK1Aq0umii1r3Ys6NneJTjoxG1UOkN/VN6iK2RXEsT3KnPmgjq4g6IDz4NSZUKB/Rc+3qlaT1QOe0vRr9rF3lv1a/4UZdghURISHWwCbtpY1vmnGFm39wZ7kqzK+n6f5cWbUoY46DeyuOlzlAABQ6lxZTtnKz80dDOdBWsI8zVxj31ZSo30XtzdSACg7dLUEAOWUL3W1VJjuh4rON7taqjJqpYJvauBxWQMnxSuoeYC0Z66OPPqiMzWvIQpePFRVAs3ukLor9WVvVrr7VldLUjMFjpumwOsbqJLZ1VLcAmV+J/l36qsqV9dygjeZyvpmlo6Om6VT1nOUJ+Wqq6Vc3QH0nzhIrb1UOe2TXS3ldDtwqToNf0K35v34/hhNnr5ORwIjFTXuLjV2Jue1f8ULmrHu12IsWyF4qxuj5FjNeHml9mdJ9Y11NdSbA7PS1VKFUq66L8mVBxTEv3ZLdb3zbnW46tzly8mfLu6oh57sVeKxT7a+87jmm9l+rjwh5zdqdtbwf3b3znFS6K7xCp9X5sxnKR3PZgXo1AXfK6NaSw18fJBaFKrhVbJW/WuK1qYYSb9L1fquIep73aXydM92zm+YQeMHn9GdHsbPQvlWnvKqUjn+CziuXcfSdGj/JsWv/kwb9toj3Qc26q2HotrnufEgv3JWAehqCSgQXS0BAM6LjIPOVXH12hX+xFIlJMROnJLOidzrMVVtGmD8zdTJOE9BB9MsufbYLScqtximKlaqIuqgaq+8rSAz6HAkQRkT2yt14gxlfjRDR0d00m8jJurEz+bFQoD8rh+q4FcmqeoF0AIEZahuqHNBfFBHzEqeiuz3o7I7PwiUv5tMZvdXcTKHSg2OaOMx6GCq37qlzEZc2hqnzeZg8b7mSJzmvu4EHW56WH/xZtAB8DH+wcHWwKnmI7vV1hW3PaFnRg5yG3Qw1anrVAP+/qt+LXGXaWcGcK1T70wkIOc3Ug6qEHGSCis4fJD6m2PLHP9e78fYA/cX7JAOOuejFvc+of75BB1MweF3q2dTM5Wh+E2F/Q2g9JT28X9w9RQ9+cTjZz3GPPusZsyJsYMOfsFq3PVhjRqSN+gAwBdwWAIASu5/h5xK+Nqq3NdKVFANVLl2sJ08sk9Op0s5/IZEqrLVt0mAqtzznXUnuKeH1SrCdFm4AlrbyYrGf1S0Aq40ljP1a6WPGqj0OOeFbD8s0LFH2uvokgRr/6l0ZQ9Ve2QwhROUnpo1nP6HD2qfNbhBBbY/yR7Pwq+OLss7nkXWDm3eYt8tmfb5q+dc0J/1MFtFOJ9J+N5lfcZnpG3S/Bnva8dxKfi6QfpLryutMSmAisa84/f5FybrmeinNfpJ+/FI1/rWa3s/W6kd+QUFLwuV/c6fzWyhZLL2a59Ts1i7bq4gX+06doBSxuvJVuKCFRYZKbOkmPHtt3af84VWRw0KFTcNVPg1YXbyf/vtfB4oS2Vw/JtB2DpXtVSHvg9r9PinFdWF8z/gq7i2BwCU3JIdyrL6yAmWXytvRx6qShc5yULyq+xU6nvbZYNV2bmpJ+vnFXYiRwMFXtvMSRdFA1W5vYeTrkgeU1BkA+Nvpk59OlYZv9hT3XG9aQYlDlvpSq0GKKiCBmLgA/z+oMZX2cndP5TCnaInjquoVfOuUhq0eccPP9qJps3ObdGwLU7xxeilYUfcRtkdGvgAM+gwbYG2OkGHEXe3pNIBF5Q6Nw+wux86vlmLlu3wnPfUbKzGF5uJNG3e8rM1qdi2faetViJMYU2shK2ukbdaB+Cv2vmDz+QSZaNeHaNkZ8jar18Kdft3sC61tk+qfv3NmlAg/8CK21YW5VApH//ZgdfcDzMIO/xvg9Qj8koFF3Is6VMZRSyhZZ0ocpkOwLkIPAAAvGCBTjrXsn4t+irIK93lGAVXK5gRIL+61iVcoVWt6XSHdOKEvFmn5z+oo9OiYZ9Orv7ampaj9TD5h5qJfXI9fY01BkdBj7R19tgOflf3MpaygukbZo8xd2qPXG8WPIZF5uw4Z1s1UOUIKwGUgmC1bGnf+5vx7ZfFqnx3JygoyE4c/tVuHVBYWfudLjYCFeTNTCAjTl9/ay9c2DUt83Tb4dLmb8xxLozX7ppwzsW828czAxRmXjX8FKfNv1sfLVu5gg41Iv9M0AEXJr+6urVfR+tO44xv39V/PQ4Af6Vah19qpdK+XFOCfC9N6/+fnXeo2bUKP2sA1z+ohTUSvbT/i8+0t5QCquVC5kkZWZMhSIGFypguVQ3rdvEMHTx0gQdtUE758vFfTYHV7VTyEbsDykI7cNAu0wUa32FNAFAcBB4AAF6wT8c32N3lKDBcAV7pLme9spw+b/2uLMr39VWVy+3ukE4f3C6vdUkeOd65g9/43p3Llb7ZSuao0i3cnkfjNzPyvObJydUJdmV7jWbyv8WaVHFcXtseODoj1c1YGG78sk1Zx5y0c4EAlIbgyJvtSvSsHVr+gTlAZ8kFh15pda2hlB3aWZSK+R92a7eVuEpXXmklvCBDWz+I0Q4zc6nRUZ1a57kV8MQmJWw3E2Fq0aqQtwlWbalwq0/x/dr8XRlXjJljOjhBB3NMh2F9w6gQwIXril66M9I8AjIUvyTGY4Vf/Vt62QMdlyDfy4hfrBVWV02XqkPXa88ZhyCsfaTd3cqRdfpw7QXc39IBp/ujwLqqYxdHCxCsxlfbzWn3frupUK3K9v7k3O1z6UVOFzdA2fLd4z9YDULtA/HIzp1FarWZ03L0yitKPCg/cCEj8AAA8I6Fo5TpDJhcqdVQXfRoBytdfF/rxE929ztq1FHVIu1kQfzu66sqzlVY1o4ZdqKkIh9T8KN9Vdm8u+/EdmVOmZGnJUVfBbawgxJZuxbqpJUqhM1LdNLqgqiWqnQdYk2qMA6n2QGH6qGqXKgWMLVVyao9ND73kzUBKB1Vr1XfXnYtf8Z38/SfNV64QL4iTE2t/Xe/NqwvbFcmGYr/Ms6uALzK+PxZdw8XV4b2xrym+d+Z3xqoiL69dEWe0n5a3Jd2v+NNW+a5Yzk//mrh9Clu3s1YZsNjJH+p2c6YDvW7/EN/Y0wHQI179LGDCkfWaeEKD/lP1Za6q5d9DFv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    }
   },
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   "id": "2a1eac1a",
   "metadata": {},
   "source": [
    "To install PyTorch with CUDA, first find out the CUDA version of your GPU, as shown at the top right corner of figure Appendix.1. My CUDA version is 11.8 so I’ll use it as an example in the installation below. If the CUDA version on your computer is neither 11.8 nor 12.1, choose the one closest to your version and it will work. For example, a computer has a CUDA version of 12.4 and someone used CUDA 12.1 and the installation was successful. \n",
    "\n",
    "If you go to the PyTorch website here, https://pytorch.org/get-started/locally/, you’ll see an interactive interface as shown in figure Appendix.2.\n",
    " ![figAppendix02_LIU.png](attachment:figAppendix02_LIU.png)\n",
    "Figure Appendix.2 The interactive interface on how to install PyTorch\n",
    "\n",
    "Once there, choose your operating system, select Conda as the Package, Python as the Language, and either CUDA 11.8 or CUDA 12.1 as your Computer Platform (based on what you have found out in the previous step). The command you need to run will be shown at the bottom panel. For example, I am using the Windows operating system, and I have CUDA 11.8 on my GPU. Therefore, the command for me is shown at the bottom panel of figure Appendix.2. If the CUDA version on your computer is neither 11.8 nor 12.1, choose the one closest to your version and it will work. For example, a computer has a CUDA version of 12.4 and someone used CUDA 12.1 and the installation was successful. \n",
    "\n",
    "Once you know what command to run to install PyTorch with CUDA, activate the virtual environment by running the following line of code in the Anaconda prompt (Windows) or a terminal (Linux):\n",
    "\n",
    "```\n",
    "conda activate dgai\n",
    "```\n",
    "\n",
    "Then, issue the line of command you have found out in the last step. For me, the command line is:\n",
    "\n",
    "```\n",
    "conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia\n",
    "```\n",
    "\n",
    "Follow the on-screen instructions to finish the installation. Here, we install three libraries together: PyTorch, Torchaudio, and Torchvision. Torchaudio is a library to process audio and signals and we need it to generate music later in this book. We'll also use the Torchvision library extensively in the book to process images.\n",
    "To make sure you have PyTorch correctly installed, run the following lines of code in a new cell in Jupyter Notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8bd852fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.1\n",
      "0.15.2\n",
      "2.0.2\n",
      "cuda\n"
     ]
    }
   ],
   "source": [
    "import torch, torchvision, torchaudio\n",
    "\n",
    "print(torch.__version__)\n",
    "print(torchvision.__version__)\n",
    "print(torchaudio.__version__)\n",
    "device=\"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ef7cd28",
   "metadata": {},
   "source": [
    "The output above says *cuda*, indicating that GPU training is available on my computer. If you don't have cuda-enabled GPU on your computer, the output is *cpu*. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e34d1fc",
   "metadata": {},
   "source": [
    "## 2.2. PyTorch concepts and methodologies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f09d3dd",
   "metadata": {},
   "source": [
    "Now that you have successfully installed PyTorch on your computer, we'll have a quick overview of the concepts and methodologies used in PyTorch in this section. You'll learn more in Chapter 2 of the book on how to conduct various operations by using PyTorch tensors. You'll also learn how to build, train, and use deep neural networks by implementing an end-to-end deep learning project in PyTorch.  \n",
    "\n",
    "PyTorch is a popular open-source machine learning library for Python, known for its flexibility and ease of use. Unlike some other libraries such as TensorFlow, PyTorch operates with dynamic (or \"imperative\") computation graphs. This means the graph is built on-the-fly as operations are performed, which allows for more intuitive coding and debugging.\n",
    "\n",
    "The fundamental data structures in PyTorch are tensors. Tensors are similar to NumPy arrays but have additional capabilities, such as GPU acceleration, which makes them suitable for high-performance computing. PyTorch's automatic differentiation engine, named 'autograd', is a key feature that allows for the automatic computation of gradients. This is essential for backpropagation and learning in neural networks. Further, PyTorch provides the *torch.nn.Module()* class to help create and train neural networks. The *torch.nn.Module()* class is a way to encapsulate parameters and helpers for forward and backward passes in neural networks, and this makes the creation of deep neural networks easy. It is the fundamental building block of all deep neural networks in this book. \n",
    "\n",
    "Training neural networks can be time-consuming, especially when the network is deep with many layers and is trained on large amounts of data. PyTorch provides GPU acceleration since it supports CUDA, which enables it to efficiently perform computations on GPU, significantly speeding up training and inference processes. PyTorch automatically detects if a CUDA-enabled GPU is installed on your machine and decides if GPU acceleration should be used in training. You'll see this line of code quite often in this book:\n",
    "\n",
    "```python\n",
    "device=\"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "```\n",
    "\n",
    "The above line of command automatically checks if a CUDA-enabled GPU is available for accelerated training. If yes, the program will use GPU to train the model. Otherwise, the program will default the training device to the CPU on your computer.\n",
    "\n",
    "These concepts and methodologies make PyTorch a versatile tool for deep learning in general and generative modeling in particular. You'll learn to use them in the chapters throughout the book."
   ]
  }
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